• Cooler than parking tickets
• Access to ~31,000 observations, over 5 years!
• Have you seen The Wire?

• Show distribution of crime in our city
• Provide an open-template for analyzing more data
• Get people excited about using R

1. Open Data Portal - Crime Reports
2. Geocode via Google Map API
3. Use library(sf) %>% tidyverse for spatial data exploration
4. Model and test patterns with library(spdep) and glm()

library(tidyverse) # duh
library(magrittr) # %<>% life
library(viridis) # it's pretty
library(sf) # the new kid
library(spdep) # the grandaddy

Top 3 address for drug crime and not drug crime

arrange(crime_counts, -n) %>% group_by(drug_flag) %>% slice(1:3)

## # A tibble: 6 x 3
## # Groups:   drug_flag [2]
##                               address drug_flag     n
##                                 <chr>     <chr> <int>
## 1  600 E MARKET ST Charlottesville VA     drugs   410
## 2   400 GARRETT ST Charlottesville VA     drugs    38
## 3 700 PROSPECT AVE Charlottesville VA     drugs    38
## 4  600 E MARKET ST Charlottesville VA not_drugs   635
## 5 700 PROSPECT AVE Charlottesville VA not_drugs   412
## 6   1100 5TH ST SW Charlottesville VA not_drugs   341

The police station's address is 606 E Market Street….

"The answer is quite simple - when individuals walk in to the police department to file a report the physical address of the department (606 E Market Street) is often used in that initial report if no other known address is available at the time. This is especially true for incidents of found or lost property near the downtown mall where there is no true known incident location. The same is true for any warrant services that result in a police report occurring at the police department." - CPD

Census blocks make a lot of sense becuase:

• Tons of data in Census and American Community Surveys
• Reputable source with code books and APIs
• Easy to access in R via ODP and library(tidycensus)

long_url <- "https://opendata.arcgis.com/datasets/e60c072dbb734454a849d21d3814cc5a_14.geojson"
census <- geojsonio::geojson_read(long_url, what = "sp") %>%
    st_as_sf()

ggplot(census, aes(fill = HU_Vacant / Housing_Units)) +
    geom_sf() + scale_fill_viridis()

• Start with geocoded version

crime <- read_csv("https://github.com/NathanCDay/cville_crime/raw/master/crime_geocode.csv")
crime %<>% filter(complete.cases(.))
crime %<>% filter(address != "600 E MARKET ST Charlottesville VA")

• Convert to sf, with same CRS (critical)

crime %<>% st_as_sf(coords = c("lon", "lat"), crs = st_crs(census))

• Use sf::st_within() and friends

crime %<>% mutate(within = st_within(crime, census) %>% as.numeric) %>% 
    filter(!is.na(within))

There are bunch of other great st_x(sf_a, sf_b) functions too. If you want to do it, there's a tool for it.

• Flag by interest

crime %<>% mutate(drug_flag = ifelse(grepl("drug", Offense, ignore.case = TRUE),
                                     "drugs", "not_drugs"))

• Summarise with tidyverse

crime_block <- st_set_geometry(crime, NULL) %>% # remove geometry for spread() to work
    group_by(within, drug_flag) %>%
    count() %>%
    spread(drug_flag, n) %>%
    mutate(frac_drugs = drugs / sum(drugs + not_drugs)) %>%
    ungroup() # geom_sf doesn't care for grouped dfs/tbls

• Join in

census %<>% inner_join(crime_block, by = c("OBJECTID" = "within"))

Since the response is the proportion of drug crime to total, i.e. bounded by 0 and 1, we want to use the quasibinomial distribution instead of the Gaussian.

##                  Estimate   Std. Error     t value     Pr(>|t|)
## (Intercept) -3.351612e+00 3.521631e-01 -9.51721491 7.500572e-11
## frac_black   1.178626e+00 3.948554e-01  2.98495478 5.397890e-03
## frac_vacant -1.181078e-01 2.220301e+00 -0.05319451 9.579076e-01
## income      -5.789340e-06 3.566446e-06 -1.62327999 1.143414e-01
## age          8.641429e-03 9.119871e-03  0.94753847 3.504655e-01

Test resid(mod) again for spatial correlation, a good model should have randomly dispearsed residuals.

##   statistic p.value parameter                            method
## 1 -0.156563   0.918        82 Monte-Carlo simulation of Moran I
##   alternative
## 1     greater